Image registration is the process of finding a transformation that aligns one image to another. Within the broad area of research, medical image registration has emerged as an active field and is becoming a valuable tool for various medical image processing applications [Lester and Arridge 1999; Periaswamy and Farid 2003; Rohlfing, Maurer et al. 2003; Cao and Ruan 2007; Zhang, Arola et al. 2010]. In other words, the importance of medical image registration is due in part to the many clinical applications including diagnosis and surgical planning, and to the need for registration across different imaging modalities such as MRI, X-RAY, UT, CT and PET.
Current image registration methods in use are either prospective or retrospective [Van Den Elsen, Pol E et al. 1993; Peitryz, Herholz et al. 1996]. Prospective registration techniques require external markers (fiducial markers) in place before the desired scan is obtained and during any subsequent scans and this methodology has proven to be logistically difficult in the clinical setting. In contrast, retrospective methods are attractive because they can be applied at any time after the acquisition of the image data and require no external devices. The retrospective techniques currently in use are surface matching [Pelizzari, Chen et al. 1987; Pelizzari, Chen et al. 1989; Jiang, Robb et al. 1992; Rusinek, Tsu et al. 1993; Turkington, Jaszczak et al. 1993], principal axes transformation [Gamboa-Aldeco, Fellingham et al. 1986; Alpert, Bradshaw et al. 1990; Toennies, Udupa et al. 1990; Holupka and Kooy 1992; Rusinek, Tsu et al. 1993], and voxel intensity matching [Wells, Viola et al. 1996; Woods, Grafton et al. 1998].
The image registration, similar to other problems in the image processing, is an optimization process to minimize a cost function that characterizes the similarity between the source and target images. Some algorithms use a complementary cost function (e.g., the regularization or penalty term to control particular deformations and constrain the transformation between the source and target images. In some registration algorithms this term is called the smoothness and biharmonic constraint penalty (or thin-plate model) term that is proportional to the deflection bending energy of a thin plate. Some other algorithm use a penalty term called the Laplacian or membrane model term that is proportional to the deflection energy of a membrane.
Current medical image registration, however, still presents many challenges. Several notable difficulties are 1) the transformation between images can vary widely and be highly nonlinear in nature; 2) the transformation between images acquired from different modalities may differ significantly in overall appearance and resolution; and 3) each imaging modality introduces its own unique challenges, making it difficult to develop a single generic registration algorithm. Some other notable difficulties include: 4) some imaging techniques such as echo planar image (EPI) data used in functional imaging, can exhibit severe localized geometric distortion; 5) nonrigid registration techniques, which are base on intensity, often produce transformations that substantially change the image (object) volume; and 6) histological comparison with in-vivo image data is complicated due to many factors in the image data such as pixel or voxel size, slice orientation, and/or the number of slices obtained. The most prominent factors affecting correlation with histological sections result from the histological fixation process that causes shrinkage, tearing, and distortion of the tissue.
Another challenging problem is with registration of radiological images acquired before and after therapy. This problem is more critical in registration of pre-contrast with post-contrast image pairs. Since tumors typically change shape and volume during treatment, the intensity-based registration algorithms may compress or expand tumors during the registration process and therefore provide results that are misleading in regard to tumor response. This miss-registration severely affects the use of the registered images in the post-processing such as image subtraction, volumetric analysis, multi-spectral classification, and pharmacokinetic modeling. This especially happens in the registration of high-resolution anatomical images with lower resolution pharmacokinetic parameters obtained from sequential Dynamic Contrast Enhanced MRI (DCE-MRI).
The above mentioned problem occurs because intensity-based registration algorithms are designed to minimize an intensity inconsistency between the two images. As the image intensity profile changes after therapy and injection of the contrast agent, a direct comparison of image intensities with sum of squared intensity differences or correlation cannot be used as an image similarity measure. In this case mutual information could be used as an alternative cost function that has been shown to accurately and robustly align images from the same patient that are acquired from different image modalities. However this solution is only an effective similarity measure for rigid registration and fails to preserve contrast-enhancing structures from substantially changing size in nonrigid registration. Hayton et al. used a modified optical flow algorithm with a constraint based on a pharmacokinetic model of the contrast uptake. The goodness of fit of the uptake model provides a consistency criterion for the registration.
A different approach specifically designed to address the problem of volume loss couples the control points of a free-form (B-spline) deformation in order to make the contrast-enhancing lesion locally rigid. Because the structures are enforced to be rigid, this approach prevents deformation of the structures even in cases where they have actually deformed. A novel physics-based regularization term to constrain the deformation has been developed by Rohlfing et al. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure (normalized mutual information) and a regularization term which is a local volume-preservation (incompressibility) constraint based on the concept that soft tissue is incompressible for small deformations and short time periods. That is, the tissue can be deformed locally, but like a gelatin-filled balloon, the volume (local and total) remains approximately constant. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. A constraint term proposed by Rohlfing et al. was combined with an extension of the adaptive bases algorithm (ABA) by Li et al. to control the compression or expansion of a tumor for registration of MR images before and after therapy. However, these regularization approaches are only designed for DCE-MR images to address the specific problem of volume loss in the nonrigid registration of contrast-enhanced images and are not suitable for multimodal image registration.
It thus would be desirable to provide new methods and systems for image registration, more specifically, reslicing-based nonrigid 3D registration of the radiological images.